A. Syrakos1, A. Sfetsos1,2, M. Politis1, G. Efthimiou1, J.G. Bartzis1
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AN EVALUATION OF THE UoWM MM5-SMOKE-CMAQ MODELING SYSTEM FOR WESTERN MACEDONIA A. Syrakos1, A. Sfetsos1,2, M. Politis1, G. Efthimiou1, J.G. Bartzis1 1Environmental Technology Laboratory, Department of Engineering and Management of Energy Resources, University of Western Macedonia, Greece 2 Environmental Research Laboratory, INT-RP, NCSR Demokritos, Greece Abstract: The University of Western Macedonia (UoWM) has recently set up an operational system for predicting air quality in the Florina–Ptolemais–Kozani basin in Western Macedonia, Greece. This is a mountainous basin where around 65% of the total energy production of Greece takes place in lignite power plants. The concentrations of PM10 is the main air quality concern in the region, and the high particulate matter levels observed are attributed to the activities related to power generation. The system set up by the UoWM makes use of software such as MM5 for weather prediction, SMOKE for emissions processing, and CMAQ for air quality prediction. This paper describes a preliminary effort to evaluate the system by comparing its predictions against ground station observations for a selection of days of the 2007 summer period. Key words: MM5, CMAQ, PM10, power plants, model evaluation, weather category. 1. INTRODUCTION The greater lignite basin of Bitola–Florina–Ptolemais–Kozani is where most of the electric power of Greece and the FYROM is produced. This is due to the abundance of lignite reserves in the region. The Greek Public Power Corporation (GPPC) operates 5 lignite power plants with a total power exceeding 4 GW, while a power plant near Bitola is the major lignite centre for the FYROM (675 MW), which covers 70% of the country’s energy needs. The basin is located in the northwest of Greece, with a mean elevation of 650 m above mean sea level (MSL), surrounded by mountains whose peaks exceed 2000 m MSL – see Figure 1. The main axis of the basin is oriented from northwest to southeast. More than 150,000 people live within the region of the basin, with the main towns being Kozani and Ptolemaida. PM10 concentrations are the main concern in the region. The main PM sources are considered to be the power plant stacks, and the open-pit lignite mines (mining, transportation of soil and coal, movement of vehicles on unpaved roads). Although PM concentrations exhibit a falling trend in recent years due to actions taken by the PPC, such as the use of new electrostatic filters for the stacks and techniques for the control of fugitive dust emissions from the mines, their levels are still high and under certain meteorological conditions may exceed local and international standards (Triantafyllou, 2003). The Laboratory of Environmental Technology of the UoWM, based in Kozani, is developing an operational system to monitor and predict air quality in the region. The system is connected to a network of monitoring stations which are operated by the PPC, the locations of some of which are shown in Figure 1. In addition the UoWM has its own PM monitoring station at the north edge of Kozani. For the prediction part of the system, the PSU/NCAR model MM5 (Grell et al, 1994) is set up to provide a meteorological prediction to the UNC/USEPA model CMAQ (Byun and Ching, 1999) which makes a 72-hour air quality prediction. The pollutant sources are provided to CMAQ by the UNC SMOKE model which processes a detailed emissions inventory for the region, prepared by the Environmental Technology Laboratory of the UoWM and NCSR Demokritos, Athens (Vlachogiannis et al, 2007). This emissions inventory is under continual improvement and apart from the power generation related sources it contains sources such as the traffic network, central heating, industry, and biogenic sources. The emissions inventory, and the CMAQ prediction, involves many pollutants but the present paper deals only with PM10, since it is of most concern. In an attempt to evaluate the system, a number of days of the April–September 2007 period were selected, each considered representative of a specific weather category, and the predictions produced by the system were compared against measurements of the monitoring stations network. A similar study will have to be performed for the winter period. 2. SYSTEM SET-UP MM5 (version 3.7.4) is set up to use 4 nested domains. Their horizontal dimensions are respectively, from coarser to finer, 39×39, 36×36, 54×54 and 72x72 cells, and the respective spatial resolutions are 54 km, 18 km, 6 km and 2 km. The finest 2×2 km domain covers the area shown in Figure 1. All domains consist of 30 vertical layers. As for the physical parameterization, the following schemes are used: x MRF PBL scheme. x Grell cumulus parameterization on all domains except the finest, where no cumulus parameterization is used. x “Simple ice” for the explicit moisture scheme. x RRTM radiation scheme. The boundary conditions are obtained from the output of the GFS model stored in the daily global repository of the National Center for Environmental Prediction (NCEP), USA. 88 For the purpose of this study, the boundary conditions and the initial conditions for the selected past dates were obtained from the NCEP “FNL Global Tropospheric Analyses” product available from http://dss.ucar.edu/datasets/ds083.2/. This product is in the form of GRIB files containing data covering the entire globe at 1×1 degree resolution (approx. 100 km), at 6-hour intervals, obtained by a combination of GFS model predictions and observations. However, in the MM5 runs data assimilation was not used to nudge the simulations towards observational data. This is expected to cause a slight degradation of the quality of the meteorological predictions. POWER PLANTS: 1. AG. DIMITRIOS (1595 MW) 2. KARDIA (1250 MW) 3. PTOLEMAIDA (620 MW) 4. AMYNTAIO (600 MW) 5. MELITI (330 MW) 6. REK BITOLA (675 MW) MONITORING STATIONS: 1. VEVI 2. AMYNTAIO 3. ANARGYROI 4. PENTAVRYSOS 5. PPC VILLAGE 6. PONTOKOMI 7. PETRANA Figure 1. The working domain, and main towns. Numbered squares indicate the power plants, and numbered circles the PPC monitoring stations. The approximate area covered by the lignite mines is shown in grey. On the other hand, CMAQ is set up to use only one domain, the finest one of the MM5 set-up, which covers the region shown in Figure 1. The grid spacing is the same as for MM5 (2×2 km) and the same vertical structure of 30 layers is retained. The power plant stacks emissions are spread among several layers, according to the plume rise prediction performed by SMOKE which uses the meteorological prediction provided by MM5. All other emissions are assumed to occur within the grid cells which are adjacent to the ground (i.e. within layer 1), which is about 35 m tall. Preliminary experiments showed no marked improvement of air quality predictions with increased resolution in the vertical direction near the ground. The CMAQ setup includes using CB-IV as the chemical mechanism, and the “aero3” aerosol module. Since boundary conditions are not available, concentrations at the lower levels of the domain boundaries were approximated using the measurements at monitoring stations near these boundaries, during days when the wind direction is from the boundary towards the interior of the domain. For each of the selected dates, the simulation was performed starting one day in advance. 3. DAYS SELECTED Each day selected corresponds to a different weather category. In previous studies concerning the region, such as Triantafyllou (2001) or Triantafyllou et al (2002), a number of weather types where identified when pollution episodes may occur. However, this approach is subjective and depends on the judgement of the meteorologist who must study the forecast and make a judgement as to which weather category it belongs to. For an operational system though it is more convenient that the weather types and the classification of forecasts into one of these types are done automatically using an algorithm. For this study a number of different weather types have been identified using a methodology based on the subtractive clustering algorithm (Chiu, 1994) coupled with the “compactness and separation criterion” (Kim et al, 2001) for identifying the optimal number of clusters (Sfetsos et al, 2005). The algorithm uses GFS model forecasts and classifies a weather forecast for a particular day using the following variables: u and v wind components at 10 m AGL, u and v wind components at 500 hPa, temperature at 2 m AGL, relative humidity at 2 m AGL, and the mixing layer height (MLH). These variables are considered at three periods of the day: 00:00, 12:00 and 24:00, and they are considered only at the two vertical columns of GFS cells which cover the Ptolemaida–Kozani basin. To derive the weather categories, the “FNL Global Tropospheric Analyses” files from NCEP for the entire 2006 and 2007 were processed. Separate categories were derived for the summer (April–September) and winter (October–March) periods. In this paper we will only deal with the summer period. Using this procedure, 11 weather categories were obtained for the summer period. Figure 2 shows the percentage of the number of days of each category for the summer periods of 2006 and 2007. Note that although category 8 is the largest category, it only appeared in 2006 and not in 2007 for which we have PM measurements data. Therefore this 89 category is not treated in the following. The days shown in Table 1 each belong to a separate weather category. The mean wind speed and wind direction is shown for each day in the same table. Figure 2. The percentage of the number of days of each summer weather category for 2006 and 2007.